Customer churn reasoning analysis model for telecommunication industry

Abstract

Customer churn is the most impactful problem in every business and industry. Therefore, every company tries their best to satisfy and maintain existing customers. Today telecommunications companies are facing this problem frequently due to increasing demand of customers every day. It is very difficult to gather new customers and need to allocate a huge cost from company revenues to acquire new customers compared to retaining the existing customer, therefore it is more important to increase their customer retention and work for that. This research is based on churn customer information and the primary objective of this research is to predict the churn reason of a given customer who has predicted to be churn using modern data analytics techniques. It include Logistic Regression, Naive Bayes, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine and Gradient Boost Classifier. Further, Hybrid Model has been considered using Voting Classifier ML model. The dataset used in this research is obtained through the Data Warehouse of one of the leading telecommunication companies.